A Video Analytics System for Class Room Surveillance Applications
Saeed Ahmed1, Nirmal Krishnnan2, Thanmay Ganta3, Gurusamy Jeyakumar4
1Saeed Ahmed, Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore (Tamil Nadu), India.
2Nirmal Krishnnan, Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore (Tamil Nadu), India.
3Thanmay Ganta, Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore (Tamil Nadu), India.
4Gurusamy Jeyakumar, Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore (Tamil Nadu), India.
Manuscript received on 22 April 2019 | Revised Manuscript received on 01 May 2019 | Manuscript Published on 08 May 2019 | PP: 65-69 | Volume-7 Issue-5S3 February 2019 | Retrieval Number: E11120275S19/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Using video analytics to give insights about events happening in classroom is a very important task in classroom surveillance systems. This paper proposes a new algorithmic framework to identify abrupt changes in a class room video and then evaluate the attention level of students. The proposed algorithm is implemented with and without video key frame extraction approaches. The SSIM (Structural Similarity Index) approach for key frame extraction is used in this study. After extracting the key frames, the detection of face and upper body of the students to evaluate their attention level is performed on the key frames. The results comparing the algorithms with and without SSIM reveals that the SSIM based algorithm gives better results. The algorithmic design of the proposed approach, the results obtained and sample cases are presented in this paper.
Keywords: Video Analytics, Key Frame Extraction, SSIM Values, Computer Vision, Face Detection, Upper Body Detection.
Scope of the Article: Data Analytics Modelling and Algorithms